• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.568-573
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• 2018

UAV(Unmanned Aerial Vehicle) is widely used in space information construction, agriculture, fisheries, weather observation, communication, and entertainment fields because they are cheaper and easier to operate than manned aircraft. In particular, UAV have attracted much attention due to the speed and cost of data acquisition in the field of spatial information construction. However, ortho image images produced using UAVs are distorted in buildings and forests. It is necessary to solve these problems in order to utilize the geospatial information field. In this study, fixed wing, rotary wing, vertical take off and landing type UAV were used to detect distortions of ortho image of UAV under various conditions, and various object areas such as construction site, urban area, and forest area were captured and analysed. Through the research, it was found that the redundancy of the unmanned aerial vehicle image is the biggest factor of the distortion phenomenon, and the higher the flight altitude, the less the distortion phenomenon. We also proposed a method to reduce distortion of orthoimage by lowering the resolution of original image using DTM (Digital Terrain Model) to improve distortion. Future high-quality unmanned aerial vehicles without distortions will contribute greatly to the application of UAV in the field of precision surveying.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.533-544
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• 2018

Rainfall is one of the most important meteorological variables in meteorology, agriculture, hydrology, natural disaster, construction, and architecture. Recently, satellite remote sensing is essential to the accurate detection, estimation, and prediction of rainfall. In this study, the accuracy of Integrated Multi-satellite Retrievals for GPM (IMERG) product, a composite rainfall information based on Global Precipitation Measurement (GPM) satellite was evaluated with ground observation data in the inland of Korea. The Automatic Weather Station (AWS)-based rainfall measurement data were used for validation. The IMERG and AWS rainfall data were collocated and compared during one year from January 1, 2016 to December 31, 2016. The coastal regions and islands were also evaluated irrespective of the well-known uncertainty of satellite-based rainfall data. Consequently, the IMERG data showed a high correlation (0.95) and low error statistics of Bias (15.08 mm/mon) and RMSE (30.32 mm/mon) in comparison to AWS observations. In coastal regions and islands, the IMERG data have a high correlation more than 0.7 as well as inland regions, and the reliability of IMERG data was verified as rainfall data.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1329-1350
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• 2018

There have been many applications to observe Earth using synthetic aperture radar (SAR) since it could acquire Earth observation data without reference to weathers or local times. However researches about digital map generation using SAR have hardly been performed due to complex raw data processing. In this study, we suggested feasibility of producing digital map using SAR stereo images. We collected two sets, which include an ascending and a descending orbit acquisitions respectively, of KOMPSAT-5 stereo dataset. In order to suggest the feasibility of digital map generation from SAR stereo images, we performed 1) rational polynomial coefficient transformation from radar geometry, 2) digital resititution using KOMPSAT-5 stereo images, and 3) validation using digital-map-derived reference points and check points. As the results of two models, root mean squared errors of XY and Z direction were less than 1m for each model. We discussed that KOMPSAT-5 stereo image could generated 1:25,000 digital map which meets a standard of the digital map. The proposed results would contribute to generate and update digital maps for inaccessible areas and wherever weather conditions are unstable such as North Korea or Polar region.

• Atmosphere
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• v.31 no.5
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• pp.577-592
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• 2021

This study evaluates the predictability of the number of days of heat and cold damages by growth stages of rice in South Korea using the hindcast data (1986~2020) produced by Pusan National University Coupled General Circulation Model-Weather Research and Forecasting (PNU CGCM-WRF) model chain. The predictability is accessed in terms of Root Mean Square Error (RMSE), Normalized Standardized Deviations (NSD), Hit Rate (HR) and Heidke Skill Score (HSS). For the purpose, the model predictability to produce the daily maximum and minimum temperatures, which are the variables used to define heat and cold damages for rice, are evaluated first. The result shows that most of the predictions starting the initial conditions from January to May (01RUN to 05RUN) have reasonable predictability, although it varies to some extent depending on the month at which integration starts. In particular, the ensemble average of 01RUN to 05RUN with equal weighting (ENS) has more reasonable predictability (RMSE is in the range of 1.2~2.6℃ and NSD is about 1.0) than individual RUNs. Accordingly, the regional patterns and characteristics of the predicted damages for rice due to excessive high- and low-temperatures are well captured by the model chain when compared with observation, particularly in regions where the damages occur frequently, in spite that hindcasted data somewhat overestimate the damages in terms of number of occurrence days. In ENS, the HR and HSS for heat (cold) damages in rice is in the ranges of 0.44~0.84 and 0.05~0.13 (0.58~0.81 and -0.01~0.10) by growth stage. Overall, it is concluded that the PNU CGCM-WRF chain of 01RUN~05RUN and ENS has reasonable capability to predict the heat and cold damages for rice in South Korea.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.177-189
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• 2022

The purpose of this study is to apply the d4PDF (Data for Policy Decision Making for Future Change) constructed from a large-scale ensemble climate simulation to estimate the probable rainfall with low frequency and high intensity. In addition, this study analyzes the uncertainty caused by the application of the frequency analysis by comparing the probable rainfall estimated using the d4PDF with that estimated using the observed data and frequency analysis at Geunsam, Imsil, Jeonju, and Jangsu stations. The d4PDF data consists of a total of 50 ensembles, and one ensemble provides climate and weather data for 60 years such as rainfall and temperature. Thus, it was possible to collect 3,000 annual maximum daily rainfall for each station. By using these characteristics, this study does not apply the frequency analysis for estimating the probability rainfall, and we estimated the probability rainfall with a return period of 10 to 1000 years by distributing 3,000 rainfall by the magnitude based on a non-parametric approach. Then, the estimated probability rainfall using d4PDF was compared with those estimated using the Gumbel or GEV distribution and the observed rainfall, and the deviation between two probability rainfall was estimated. As a result, this deviation increased as the difference between the return period and the observation period increased. Meanwhile, the d4PDF reasonably suggested the probability rainfall with a low frequency and high intensity by minimizing the uncertainty occurred by applying the frequency analysis and the observed data with the short data period.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.27 no.4
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• pp.385-397
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• 1982

The climatic impacts have been the environmental constraints with soil characteristics to achieve self sufficiency of food production in Korea. In this paper, the distribution and appearance of impacts and the changes in climatological status due to recent trend of early transplanting of rice are widely discussed to derive some countermeasures against the impacts, being focussed on cultural A long term analysis of the climatic impact appearances of the last 74 years showed that drought, strong wind, flood, cold spell and frost were the major impacts. Before 1970's, the drought damage was the greatest among the climatic impacts; however, the expansion and improvement of irrigation and drainage system markedly decreased the damage of drought and heavy rain. The appearance of cold damage became more frequent than before due to introduction of early transplanting for more thermophilic new varieties. Tongillines which were from Indica and Japonica crosses throw more attention to cold damage for high yields to secure high temperature in heading and ripening stages and lead weakness to cold and drought damage in early growth stage after transplanting. The plants became subject to heavy rain in ripening stage also. For the countermeasures against cold damage, the rational distribution of adequate varieties according to the regional climatic conditions and planting schedule should be imposed on the cultivation. A detoured water way to increase water temperature might be suggestable in the early growth stage. Heavy application of phosphate to boost rooting and tillering also would be a nutritional control method. In the heading and ripening stages, foliar application of phosphate and additional fertilization of silicate might be considerable way of nutritional control. Since the amount of solar radiation and air temperature in dry years were high, healthy plants for high yield could be obtained; therefere, the expansion of irrigation system and development of subsurface water should be performed as one of the national development projects. To minimize the damage of strong wind and rainfall, the rational distribution of varieties with different growing periods in the area where the damage occurred habitualy should be considered with installation of wind breaks. Not only vertical windbreaks but also a horizontal wind break using a net might be a possible way to decrease the white heads in rice field by dry wind. Finally, to establish the integrated countermeasures against the climatic impacts, the detailed interpretation on the regional climatic conditions should be conducted to understand distribution and frequency of the impacts. The expansion of observation net work for agricultural meteorology and development of analysis techniques for meteorological data must be conducted in future together with the development of the new cultural techniques.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.368-375
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• 2023

In this study, we examined the error characteristic and bias correction method for one-month temperature forecast data produced through joint development between the Rural Development Administration and the H ong Kong University of Science and Technology. For this purpose, hindcast data from 2013 to 2021, weather observation data, and various environmental information were collected and error characteristics under various environmental conditions were analyzed. In the case of maximum and minimum temperatures, the higher the elevation and latitude, the larger the forecast error. On average, the RMSE of the forecast data corrected by the linear regression model and the XGBoost decreased by 0.203, 0.438 (maximum temperature) and 0.069, 0.390 (minimum temperature), respectively, compared to the uncorrected forecast data. Overall, XGBoost showed better error improvement than the linear regression model. Through this study, it was found that errors in prediction data are affected by topographical conditions, and that machine learning methods such as XGBoost can effectively improve errors by considering various environmental factors.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.67-74
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• 2024

In this paper, we present a study aimed at analyzing how different rainfall measurement methods affect the performance of reservoir water level predictions. This work is particularly timely given the increasing emphasis on climate change and the sustainable management of water resources. To this end, we have employed rainfall data from ASOS, AWS, and Thiessen Network-based measures provided by the KMA Weather Data Service to train our neural network models for reservoir yield predictions. Our analysis, which encompasses 34 reservoirs in Jeollabuk-do Province, examines how each method contributes to enhancing prediction accuracy. The results reveal that models using rainfall data based on the Thiessen Network's area rainfall ratio yield the highest accuracy. This can be attributed to the method's accounting for precise distances between observation stations, offering a more accurate reflection of the actual rainfall across different regions. These findings underscore the importance of precise regional rainfall data in predicting reservoir yields. Additionally, the paper underscores the significance of meticulous rainfall measurement and data analysis, and discusses the prediction model's potential applications in agriculture, urban planning, and flood management.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.3
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• pp.135-142
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• 2016

The aim of this study was to confirm the improvement of efficiency for temperature estimation at 0600 and 1500 LST by using a simple method for estimating temperature lapse rate modulated by the amount of clouds in comparison with the case adopting the existing single temperature lapse rate ($-6.5^{\circ}C/km$ or $-9^{\circ}C/km$). A catchment of the 'Hadong Watermark2,' which includes Hadong, Gurye, and Gwangyang was selected as the area for evaluating the practicality of the temperature lapse rate estimation method. The weather data of 0600 and 1500 LST at 12 weather observation sites within the catchment were collected during the entire year of 2015. Also, the 'sky condition' of digital forecast products of KMA in 2015 ($5{\times}5km$ lattice resolution) were overlapped with the catchment of the 'Hadong Watermark2,' to calculate the spatial average value within the catchment, which were used to simulate the 0600 and 1500 LST temperature lapse rate of the catchment. The estimation errors of the temperatures at 0600 LST were ME $-0.39^{\circ}C$ and RMSE $1.45^{\circ}C$ in 2015, when applying the existing temperature lapse rate. Using the estimated temperature lapse rate, they were improved to ME $-0.19^{\circ}C$ and RMSE $1.32^{\circ}C$. At 1500 LST, the effect of the improvements found from the comparison between the existing temperature lapse rate and the estimated temperature lapse rate were minute, because the estimated lapse rate of clear days is not very different from the existing lapse rate. However, the estimation errors of the temperatures at 1500 LST during cloudy days were improved from ME $-0.69^{\circ}C$, RMSE $1.54^{\circ}C$ to ME $-0.51^{\circ}C$, RMSE $1.19^{\circ}C$.